Section: Overall Objectives

Privacy-aware decentralized computations

Gossip algorithms have also been studied for more complex global tasks, such as computation of network statistics or, more generally, aggregation functions of input values of the nodes (e.g., sum, average, or max). We plan to pursue this research direction both from a theoretical and from a practical perspective. We provide two examples of these directions below.

Computational capabilities of gossip. On the theoretical side, we have recently started to study gossip protocols for the assignment of unique IDs from a small range to all nodes (known as the renaming problem) and computing the rank of the input value of each node. We plan to further investigate the class of global tasks that can be solved efficiently by gossip protocols.

Private computations on decentralized data. On a more practical track, we aim to explore the use of gossip protocols for decentralized computations on privacy sensitive data. Recent research on private data bases, and on homomorphic encryption, has demonstrated the possibility to perform complex operations on encrypted data. Yet, existing systems have concentrated on relatively small-scale applications. In the coming years, we instead plan to investigate the possibility to build a framework for querying and performing operations for large-scale decentralized data stores. To achieve this, we plan to disseminate queries in an epidemic fashion through a network of data sources distributed on a large scale while combining privacy preserving techniques with decentralized computations. This would, for example, enable the computation of statistical measures on large quantities of data without needing to access and disclose each single data item.